loglik_gda: Gaussian errors with diagonal adjustment

In outerbase: Outer Product Regression

Gaussian errors with diagonal adjustment

Description

loglik = new(loglik_gda, om, terms, y, x)

This is a standard model which has the form

y = \langle φ(x), θ \rangle + δ(x) + \varepsilon, δ(x) \sim N(0, λ g(x)), \varepsilon \sim N(0,σ^2)

where φ(x) is the basis, θ is the coefficient vector, δ(x) is unseen vector corresponding to unmodeled variance λ g(x), \varepsilon is an unseen noise vector. The parameter vector is of length 2 where σ= exp(para[0]) and λ=exp(2*para[1]).

Arguments

om	an outermod instance to be referred to
terms	a matrix of terms, must have as many columns as dims in om
y	a vector of observations
x	a matrix of predictors, must have as many columns as dims in om and the same number of rows as y

Value

no returns, this is a class which contains methods

See Also

base class: lpdf

outerbase documentation built on June 9, 2022, 5:08 p.m.